Two months on the tools every analyst uses daily: statistics, Microsoft Excel, data cleaning, SQL fundamentals, basic Python and your first dashboards.
Four months from spreadsheet to production dashboard: advanced SQL, database design, Python with NumPy and Pandas, Power BI, ETL, business analytics and KPI reporting.
The complete 24-week roadmap: statistics, Excel, SQL, Python, Power BI and Tableau, ETL, business analytics, machine learning, four industry capstones and data storytelling.
The Curriculum
The Starter plan follows phases 1 to 3 of the 24-week roadmap, plus basic Python and visualization. Each block ends with an assignment.
Fundamentals — what is data analytics, descriptive, diagnostic, predictive and prescriptive analytics, the data lifecycle, analyst roles and responsibilities, industry use cases
Statistics basics — mean, median, mode, standard deviation, variance, probability, correlation, hypothesis testing, sampling techniques
Excel fundamentals — formulas and functions, data cleaning, conditional formatting, data validation, VLOOKUP, XLOOKUP and INDEX-MATCH
Advanced Excel (basics) — pivot tables, pivot charts, dashboards, what-if analysis, an introduction to Power Query
Database basics — database concepts, ER diagrams, tables and relationships
SQL — SELECT statements, WHERE, ORDER BY, GROUP BY, HAVING and JOINS, queried against MySQL
Variables, loops, functions, lists and dictionaries, plus basic charting. NumPy, Pandas, Power BI, ETL, business analytics and machine learning are covered in the Professional and Career Pro plans.
Fundamentals — descriptive, diagnostic, predictive and prescriptive analytics, data lifecycle, industry use cases
Statistics — mean, median, mode, standard deviation, variance, probability, correlation, hypothesis testing
Fundamentals — formulas and functions, data cleaning, conditional formatting, VLOOKUP, XLOOKUP, INDEX-MATCH
Advanced Excel — pivot tables and charts, dashboards, what-if analysis, Power Query, Power Pivot
Database basics and SQL — ER diagrams, tables and relationships, SELECT, WHERE, ORDER BY, GROUP BY, HAVING, JOINS
Advanced SQL — subqueries, window functions, common table expressions, views, stored procedures
Python and NumPy — variables, loops, functions, lists, dictionaries, file handling, arrays, mathematical operations
Pandas and visualization — DataFrames, cleaning, filtering, grouping, merging, plus Matplotlib, Seaborn and Plotly
Power BI — interface, data import, data modeling, DAX basics, interactive dashboards
Tableau (basics) — data connections, visualizations, filters, dashboards
Data preprocessing, handling missing values, duplicate removal, data transformation, ETL concepts and Power Query pipelines.
Business metrics — KPI design, customer acquisition cost, customer lifetime value, retention rate, revenue analysis
Case studies — retail, finance, healthcare and marketing analytics
An introduction to supervised and unsupervised learning, regression, classification and model evaluation, followed by two capstone projects and a one-month internship.
SQL extraction, Python cleaning and a published Power BI report.
Attrition, headcount and cost KPIs modelled with DAX.
Fundamentals — what is data analytics, descriptive, diagnostic, predictive and prescriptive analytics, the data lifecycle, analyst roles, industry use cases
Statistics basics — mean, median, mode, standard deviation, variance, probability, correlation, hypothesis testing, sampling techniques
Excel fundamentals — formulas and functions, data cleaning, conditional formatting, data validation, VLOOKUP, XLOOKUP, INDEX-MATCH
Advanced Excel — pivot tables, pivot charts, dashboards, what-if analysis, Power Query, Power Pivot
Database basics and SQL — database concepts, ER diagrams, tables and relationships, SELECT, WHERE, ORDER BY, GROUP BY, HAVING, JOINS
Advanced SQL — subqueries, window functions, common table expressions, views, stored procedures
Python and NumPy — variables, loops, functions, lists, dictionaries, file handling, arrays, mathematical operations, data manipulation
Pandas and visualization — DataFrames, cleaning, filtering, grouping, merging, plus Matplotlib, Seaborn and Plotly
Power BI — interface, data import, data modeling, DAX, interactive dashboards
Tableau — data connections, visualizations, filters, dashboards
Data preprocessing, missing values handling, duplicate removal, data transformation, ETL concepts and Power Query.
Business metrics — KPI design, customer acquisition cost, customer lifetime value, retention rate, revenue analysis
Case studies — retail analytics, finance analytics, healthcare analytics, marketing analytics
Concepts — supervised and unsupervised learning, regression, classification, clustering, model evaluation
Mini projects — house price prediction · customer churn prediction
Students should complete at least three projects; the Career Pro plan delivers four industry capstones with data storytelling and presentations.
End-to-end SQL extraction, automated Python cleaning pipeline, and published Power BI executive report.
Attrition, employee headcount, departmental performance and salary cost KPIs modelled with DAX.
Multi-channel audience engagement, sentiment analysis, campaign ROI, and conversion attribution.
A reviewed analyst resume built around your dashboards and SQL work.
An Excel and SQL screening round and a case round, each with written feedback.
Your assignments packaged into a portfolio you can send to employers.
Access to the hiring partn
Seven reviewed repositories with notebooks, SQL scripts and dashboard files.
A rewritten resume plus LinkedIn optimization for analyst and BI roles.
SQL, Excel, case study and dashboard walkthrough rounds with feedback.
Referrals ahead of Starter candidates, plus limited one-on-one mentorship.
A live analytics team with real reporting ownership, reviewed weekly.
E-commerce, HR, social media and financial analytics, each presented to a panel.
SQL, Python, Power BI, case study and storytelling rounds with written feedback.
Unlimited one-on-one mentorship, resume and LinkedIn optimization, priority referrals.